Appel: Elsevier KBS special issue on Big Data for Social Analysis

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Sun Sep 1 19:48:31 UTC 2013

Date: Sun, 1 Sep 2013 16:13:52 +0800
From: Erik Cambria <cambria at>
Message-ID: <FB9C4C26-72F4-41E5-AF00-0672F9060DFA at>

Apologies for cross-posting,
Submissions are invited for an Elsevier Knowledge-Based Systems special
issue on Big Data for Social Analysis.

The textual information available on the Web can be broadly grouped into
two main categories: facts and opinions. Facts are objective expressions
about entities or events. Opinions are usually subjective expressions
that describe people's sentiments, appraisals, or feelings towards such
entities and events. Much of the existing research on textual
information processing has been focused on mining and retrieval of
factual information, e.g., text classification, text recognition, text
clustering, and many other text mining and natural language processing
(NLP) tasks. Little work had been done on the processing of opinions
until only recently.

One of the main reasons for the lack of study on opinions is the fact
that there was little opinionated text available before the recent
passage from a read-only to a read-write Web. Before that, in fact, when
people needed to make a decision, they typically asked for opinions from
friends and family. Similarly, when organizations wanted to find the
opinions or sentiments of the general public about their products and
services, they had to specifically ask people by conducting opinion
polls and surveys.

However, with the advent of the Social Web, the way people express their
views and opinions has dramatically changed. They can now post reviews
of products at merchant sites and express their views on almost anything
in Internet forums, discussion groups, and blogs. Such online
word-of-mouth behavior represents new and measurable sources of
information with many practical applications. Nonetheless, finding
opinion sources and monitoring them can be a formidable task because
there are a large number of diverse sources and each source may also
have a huge volume of opinionated text.

In many cases, in fact, opinions are hidden in long forum posts and
blogs. It is extremely time-consuming for a human reader to find
relevant sources, extract related sentences with opinions, read them,
summarize them, and organize them into usable forms. Thus, automated
opinion discovery and summarization systems are needed. Big social data
analysis grows out of this need and it includes disciplines such as
social network analysis, multimedia management, social media analytics,
trend discovery, and opinion mining. The opportunity to capture the
opinions of the general public about social events, political movements,
company strategies, marketing campaigns, and product preferences, in
particular, has raised growing interest both within the scientific

All the opinion-mining tasks, however, are very challenging. Our
understanding and knowledge of the problem and its solution are still
limited. The main reason is that it is a NLP task, and NLP has no easy
problems. Another reason may be due to our popular ways of doing
research. So far, in fact, researchers have probably relied too much on
traditional machine-learning algorithms. Some of the most effective
machine-learning algorithms, in fact, produce no human understandable
results such that, although they may achieve improved accuracy, little
about how and why is known, apart from some superficial knowledge gained
in the manual feature engineering process. All such approaches,
moreover, rely on syntactical structure of text, which is far from the
way human mind processes natural language.

Articles are thus invited in area of knowledge-based systems for big
social data analysis. The broader context of the Special Issue
comprehends artificial intelligence, knowledge representation and
reasoning, natural language processing, and data mining. Topics include,
but are not limited to:
- Knowledge-based systems for big social data analysis
- Biologically inspired opinion mining
- Concept-level opinion and sentiment analysis
- Knowledge-based systems for social media retrieval and analysis
- Knowledge-based systems for social media marketing
- Social network modeling, simulation, and visualization
- Semantic multi-dimensional scaling for sentiment analysis
- Knowledge-based systems for patient opinion mining
- Sentic computing
- Multilingual and multimodal sentiment analysis
- Multimodal fusion for continuous interpretation of semantics
- Knowledge-based systems for time-evolving sentiment tracking
- Knowledge-based systems for cognitive agent-based computing
- Human-agent, -computer, and -robot interaction
- Domain adaptation for sentiment classification
- Affective common-sense reasoning
- Knowledge-based systems for user profiling and personalization

The Special Issue also welcomes papers on specific application domains
of knowledge-based systems for big social data analysis, e.g., influence
networks, customer experience management, intelligent user interfaces,
multimedia management, computer-mediated human-human communication,
enterprise feedback management, surveillance, art.

November 1st, 2013: Paper submission deadline
December 1st, 2013: Notification of acceptance
January 1st, 2013: Final manuscript due
March/April, 2014: Publication

The Special Issue will consist of papers on novel methods and approaches
that further develop and apply knowledge-based techniques in the context
of natural language processing and big social data analysis. Some papers
may survey various aspects of the topic. The balance between these will
be adjusted to maximize the issue's impact. All articles are expected to
successfully negotiate the standard review procedures for Elsevier
Knowledge-Based Systems. Contributions are invited in the form of
original high-quality research and review papers (preferably no more
than 20 double line spaced manuscript pages, including tables and
figures), following the formatting style for Elsevier. A submission that
has already been published in conference proceedings has to be submitted
as more than 45% update in comparison to the published version. The
title page should not include name, affiliation, and e-mail address of
the authors. All paper has to be submitted through thejournal electronic
submission EES via the dedicated special issue.

- Erik Cambria, National University of Singapore (Singapore)
- Haixun Wang, Google Research (USA)
- Bebo White, Stanford University (USA)
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